Nonlinear complexity of human biodynamics engine

2010 ◽  
Vol 61 (1-2) ◽  
pp. 123-139 ◽  
Author(s):  
Vladimir G. Ivancevic
Keyword(s):  
Nonlinear Complexity

scholarly journals Using Bidimensional Multiscale Entropy Analysis of Ultrasound Images to Assess the Effect of Various Walking Intensities on Plantar Soft Tissues

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 264
Author(s):  
Ben-Yi Liau ◽  
Fu-Lien Wu ◽  
Keying Zhang ◽  
Chi-Wen Lung ◽  
Chunmei Cao ◽  
...  
Keyword(s):  
Soft Tissues ◽  
Linear Analysis ◽  
Multiscale Entropy ◽  
Ultrasound Images ◽  
Entropy Analysis ◽  
Nonlinear Complexity ◽  
Human Movements ◽  
Before And After ◽  
Walking Performance ◽  
Linear And Nonlinear

Walking performance is usually assessed by linear analysis of walking outcome measures. However, human movements consist of both linear and nonlinear complexity components. The purpose of this study was to use bidimensional multiscale entropy analysis of ultrasound images to evaluate the effects of various walking intensities on plantar soft tissues. Twelve participants were recruited to perform six walking protocols, consisting of three speeds (slow at 1.8 mph, moderate at 3.6 mph, and fast at 5.4 mph) for two durations (10 and 20 min). A B-mode ultrasound was used to assess plantar soft tissues before and after six walking protocols. Bidimensional multiscale entropy (MSE2D) and the Complexity Index (CI) were used to quantify the changes in irregularity of the ultrasound images of the plantar soft tissues. The results showed that the CI of ultrasound images after 20 min walking increased when compared to before walking (CI4: 0.39 vs. 0.35; CI5: 0.48 vs. 0.43, p < 0.05). When comparing 20 and 10 min walking protocols at 3.6 mph, the CI was higher after 20 min walking than after 10 min walking (CI4: 0.39 vs. 0.36, p < 0.05; and CI5: 0.48 vs. 0.44, p < 0.05). This is the first study to use bidimensional multiscale entropy analysis of ultrasound images to assess plantar soft tissues after various walking intensities.

scholarly journals The source of discrepancies in aerosol–cloud–precipitation interactions between GCM and A-Train retrievals

2016 ◽  
Vol 16 (23) ◽  
pp. 15413-15424 ◽  
Author(s):  
Takuro Michibata ◽  
Kentaroh Suzuki ◽  
Yousuke Sato ◽  
Toshihiko Takemura
Keyword(s):  
General Circulation ◽  
Climate Models ◽  
Circulation Model ◽  
Cloud Droplet ◽  
Systematic Difference ◽  
Liquid Water Path ◽  
Light Rain ◽  
Aerosol Cloud ◽  
Nonlinear Complexity ◽  
Aerosol Cloud Interactions

Abstract. Aerosol–cloud interactions are one of the most uncertain processes in climate models due to their nonlinear complexity. A key complexity arises from the possibility that clouds can respond to perturbed aerosols in two opposite ways, as characterized by the traditional “cloud lifetime” hypothesis and more recent “buffered system” hypothesis. Their importance in climate simulations remains poorly understood. Here we investigate the response of the liquid water path (LWP) to aerosol perturbations for warm clouds from the perspective of general circulation model (GCM) and A-Train remote sensing, through process-oriented model evaluations. A systematic difference is found in the LWP response between the model results and observations. The model results indicate a near-global uniform increase of LWP with increasing aerosol loading, while the sign of the response of the LWP from the A-Train varies from region to region. The satellite-observed response of the LWP is closely related to meteorological and/or macrophysical factors, in addition to the microphysics. The model does not reproduce this variability of cloud susceptibility (i.e., sensitivity of LWP to perturbed aerosols) because the parameterization of the autoconversion process assumes only suppression of rain formation in response to increased cloud droplet number, and does not consider macrophysical aspects that serve as a mechanism for the negative responses of the LWP via enhancements of evaporation and precipitation. Model biases are also found in the precipitation microphysics, which suggests that the model generates rainwater readily even when little cloud water is present. This essentially causes projections of unrealistically frequent and light rain, with high cloud susceptibilities to aerosol perturbations.

scholarly journals On Nonlinear Complexity and Shannon’s Entropy of Finite Length Random Sequences

Entropy ◽  
2015 ◽  
Vol 17 (4) ◽  
pp. 1936-1945 ◽  
Author(s):  
Lingfeng Liu ◽  
Suoxia Miao ◽  
Bocheng Liu
Keyword(s):  
Finite Length ◽  
Shannon’S Entropy ◽  
Random Sequences ◽  
Shannon's Entropy ◽  
Nonlinear Complexity

Sources of nonlinearity and complexity in geomorphic systems

2003 ◽  
Vol 27 (1) ◽  
pp. 1-23 ◽  
Author(s):  
Jonathan D. Phillips
Keyword(s):  
Nonlinear Dynamics ◽  
Landscape Management ◽  
Surface Processes ◽  
Self Organization ◽  
Complex Behavior ◽  
Historical Evidence ◽  
Nonlinear Complexity ◽  
Storage Effects ◽  
Analytical Tools ◽  
Geomorphic Processes

Nonlinearity is common in geomorphology, though not present or relevant in every geomorphic problem. It is often ignored, sometimes to the detriment of understanding surface processes and landforms. Nonlinearity opens up possibilities for complex behavior that are not possible in linear systems, though not all nonlinear systems are complex. Complex nonlinear dynamics have been documented in a number of geomorphic systems, thus nonlinear complexity is a characteristic of real-world landscapes, not just models. In at least some cases complex nonlinear dynamics can be directly linked to specific geomorphic processes and controls. Nonlinear complexities pose obstacles for some aspects of prediction in geomorphology, but provide opportunities and tools to enhance predictability in other respects. Methods and theories based on or grounded in complex nonlinear dynamics are useful to geomorphologists. These nonlinear frameworks can explain some phenomena not otherwise explained, provide better or more appropriate analytical tools, improve the interpretation of historical evidence and usefully inform modeling, experimental design, landscape management and environmental policy. It is also clear that no nonlinear formalism (and, as of yet, no other formalism) provides a universal meta-explanation for geomorphology. The sources of nonlinearity in geomorphic systems largely represent well-known geomorphic processes, controls and relationships that can be readily observed. A typology is presented, including thresholds, storage effects, saturation and depletion, self-reinforcing feedback, self-limiting processes, competitive feedbacks, multiple modes of adjustment, self-organization and hysteresis.

HIGHLY NONLINEAR COMPLEXITY OF INTERACTION DYNAMICS IN SCALE-FREE NETWORKS

2011 ◽  
Vol 22 (09) ◽  
pp. 883-895 ◽  
Author(s):  
SHAOTING TANG ◽  
XIN JIANG ◽  
SEN PEI ◽  
ZHICONG LIU ◽  
LILI MA ◽  
...  
Keyword(s):  
Strong Dependence ◽  
Complex Structure ◽  
Finite Capacity ◽  
Scale Free ◽  
Interaction Dynamics ◽  
Inference Problems ◽  
Interactive Behavior ◽  
Routing Strategy ◽  
Nonlinear Complexity ◽  
Highly Nonlinear

Interaction dynamics induced by finite-capacity effect is a typical diffusion model on networks. Although several significant properties of it like condensation have been well studied, the origin of its highly nonlinear complexity, influenced by complex structure and interactive behavior, is still missing. Aimed at filling this gap, we propose entropy to quantify the complexity and exhibit its strong dependence on two coupled crucial elements of nodes, the local topology and the finite capacity. Further, the performance of interaction dynamics, efficiency function is also analyzed in terms of entropy rate and relatively promoted through routing strategy arguments supported by maximum entropy theory studies. This will play a crucial role for inference problems emerging in the field of interaction dynamics on complex networks.

Study of the Dynamic Characteristics of BWE Based on Products, Difference With a Two-Stage Supply Chain

2019 ◽  
Vol 12 (2) ◽  
pp. 1-21
Author(s):  
Weiya Di ◽  
Junhai Ma ◽  
Xueli Zhan
Keyword(s):  
Supply Chain ◽  
Behavioral Science ◽  
Bullwhip Effect ◽  
Mixed Duopoly ◽  
Negative Effects ◽  
Stability Range ◽  
Nonlinear Complexity ◽  
Science Management ◽  
Numerical Simulation Methods ◽  
The Stability

In previous studies, many researchers have achieved significant success in reducing the negative effects brought about by the bullwhip effect. In this article, the authors have established a supply chain that consists of one supplier and two retailers and has adopted a Cournot-Bertrand mixed duopoly model that successfully combines a nonlinear complexity dynamic system with the bullwhip effect. In order to partition systems with various degrees of disorder and take appropriate methods to refrain systems from falling into a chaos state, numerical simulation methods are conducted to find the stability range, bifurcation range and the chaos range. This article focuses on three different ranges that the authors analyze about the bullwhip effect and inventory variability. In the end, some practical suggestions on behavioral science management are made.

Nonlinear Complexity and Chaotic Behaviors on Finite-Range Stochastic Epidemic Financial Dynamics

2019 ◽  
Vol 29 (06) ◽  
pp. 1950083
Author(s):  
Guochao Wang ◽  
Shenzhou Zheng ◽  
Jun Wang
Keyword(s):  
Time Series ◽  
Financial Markets ◽  
Financial Time Series ◽  
Logistic Map ◽  
Entropy Method ◽  
Finite Range ◽  
Price Model ◽  
Nonlinear Complexity ◽  
Proposed Model ◽  
Real Market

In this paper, a novel stochastic financial price model, based on the theory of finite-range stochastic interacting epidemic system, is proposed to reproduce the nonlinear dynamic mechanism of price fluctuations in financial markets. To better understand the complexity behavior of the proposed model, we develop a new entropy-based approach called index fluctuation fuzzy entropy (IFFE) and construct four measure criteria. The effectiveness of this approach is experimentally validated by logistic map time series, white noise time series, [Formula: see text] noise time series and six financial time series. Moreover, the largest Lyapunov exponents and Kolmogorov–Sinai entropy method are applied to analyze the chaotic property of the proposed model. To verify the rationality of the proposed model, the same analyses for the real market data are comparatively investigated with the simulation ones. The empirical results reveal that the novel financial price model is able to reproduce some important features of the financial markets.

Sign in / Sign up

Export Citation Format

Share Document